Search Results for author: Wei Bi

Found 36 papers, 14 papers with code

Event Extraction as Machine Reading Comprehension

no code implementations EMNLP 2020 Jian Liu, Yubo Chen, Kang Liu, Wei Bi, Xiaojiang Liu

ii) Our model is excelled in the data-scarce scenario, for example, obtaining 49. 8{\%} in F1 for event argument extraction with only 1{\%} data, compared with 2. 2{\%} of the previous method.

Event Argument Extraction Event Extraction +3

Set Generation Networks for End-to-End Knowledge Base Population

no code implementations EMNLP 2021 Dianbo Sui, Chenhao Wang, Yubo Chen, Kang Liu, Jun Zhao, Wei Bi

In this paper, we formulate end-to-end KBP as a direct set generation problem, avoiding considering the order of multiple facts.

Knowledge Base Population Machine Translation

Spatial Entropy Regularization for Vision Transformers

no code implementations9 Jun 2022 Elia Peruzzo, Enver Sangineto, Yahui Liu, Marco De Nadai, Wei Bi, Bruno Lepri, Nicu Sebe

In this paper, we explicitly encourage the emergence of this spatial clustering as a form of training regularization, this way including a self-supervised pretext task into the standard supervised learning.

Semantic Segmentation

Breaking the Chain of Gradient Leakage in Vision Transformers

1 code implementation25 May 2022 Yahui Liu, Bin Ren, Yue Song, Wei Bi, Nicu Sebe, Wei Wang

However, simply removing the PEs may not only harm the convergence and accuracy of ViTs but also places the model at more severe privacy risk.

Federated Learning

Lexical Knowledge Internalization for Neural Dialog Generation

1 code implementation ACL 2022 Zhiyong Wu, Wei Bi, Xiang Li, Lingpeng Kong, Ben Kao

We propose knowledge internalization (KI), which aims to complement the lexical knowledge into neural dialog models.

Contrastive Learning

A Model-Agnostic Data Manipulation Method for Persona-based Dialogue Generation

1 code implementation ACL 2022 Yu Cao, Wei Bi, Meng Fang, Shuming Shi, DaCheng Tao

To alleviate the above data issues, we propose a data manipulation method, which is model-agnostic to be packed with any persona-based dialogue generation model to improve its performance.

Dialogue Generation

Event Transition Planning for Open-ended Text Generation

no code implementations Findings (ACL) 2022 Qintong Li, Piji Li, Wei Bi, Zhaochun Ren, Yuxuan Lai, Lingpeng Kong

Open-ended text generation tasks, such as dialogue generation and story completion, require models to generate a coherent continuation given limited preceding context.

Dialogue Generation Story Completion

Efficient Training of Visual Transformers with Small Datasets

1 code implementation NeurIPS 2021 Yahui Liu, Enver Sangineto, Wei Bi, Nicu Sebe, Bruno Lepri, Marco De Nadai

This task encourages the VTs to learn spatial relations within an image and makes the VT training much more robust when training data are scarce.

Inductive Bias

Good for Misconceived Reasons: An Empirical Revisiting on the Need for Visual Context in Multimodal Machine Translation

no code implementations ACL 2021 Zhiyong Wu, Lingpeng Kong, Wei Bi, Xiang Li, Ben Kao

A neural multimodal machine translation (MMT) system is one that aims to perform better translation by extending conventional text-only translation models with multimodal information.

Multimodal Machine Translation Translation

REAM$\sharp$: An Enhancement Approach to Reference-based Evaluation Metrics for Open-domain Dialog Generation

no code implementations30 May 2021 Jun Gao, Wei Bi, Ruifeng Xu, Shuming Shi

We first clarify an assumption on reference-based metrics that, if more high-quality references are added into the reference set, the reliability of the metric will increase.

Open-Domain Dialog

Learning from My Friends: Few-Shot Personalized Conversation Systems via Social Networks

no code implementations21 May 2021 Zhiliang Tian, Wei Bi, Zihan Zhang, Dongkyu Lee, Yiping Song, Nevin L. Zhang

The task requires models to generate personalized responses for a speaker given a few conversations from the speaker and a social network.

Meta-Learning

Predicting Events in MOBA Games: Prediction, Attribution, and Evaluation

no code implementations17 Dec 2020 Zelong Yang, Yan Wang, Piji Li, Shaobin Lin, Shuming Shi, Shao-Lun Huang, Wei Bi

The multiplayer online battle arena (MOBA) games have become increasingly popular in recent years.

TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching

1 code implementation COLING 2020 Heng Gong, Yawei Sun, Xiaocheng Feng, Bing Qin, Wei Bi, Xiaojiang Liu, Ting Liu

Although neural table-to-text models have achieved remarkable progress with the help of large-scale datasets, they suffer insufficient learning problem with limited training data.

Few-Shot Learning Language Modelling +2

Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems

1 code implementation COLING 2020 Jian Wang, Junhao Liu, Wei Bi, Xiaojiang Liu, Kejing He, Ruifeng Xu, Min Yang

To conquer these limitations, we propose a Dual Dynamic Memory Network (DDMN) for multi-turn dialog generation, which maintains two core components: dialog memory manager and KB memory manager.

Response-Anticipated Memory for On-Demand Knowledge Integration in Response Generation

no code implementations ACL 2020 Zhiliang Tian, Wei Bi, Dongkyu Lee, Lanqing Xue, Yiping Song, Xiaojiang Liu, Nevin L. Zhang

In previous work, the external document is utilized by (1) creating a context-aware document memory that integrates information from the document and the conversational context, and then (2) generating responses referring to the memory.

Informativeness Response Generation

A Batch Normalized Inference Network Keeps the KL Vanishing Away

1 code implementation ACL 2020 Qile Zhu, Jianlin Su, Wei Bi, Xiaojiang Liu, Xiyao Ma, Xiaolin Li, Dapeng Wu

Variational Autoencoder (VAE) is widely used as a generative model to approximate a model's posterior on latent variables by combining the amortized variational inference and deep neural networks.

Dialogue Generation Language Modelling +2

Learning to Select Bi-Aspect Information for Document-Scale Text Content Manipulation

1 code implementation24 Feb 2020 Xiaocheng Feng, Yawei Sun, Bing Qin, Heng Gong, Yibo Sun, Wei Bi, Xiaojiang Liu, Ting Liu

In this paper, we focus on a new practical task, document-scale text content manipulation, which is the opposite of text style transfer and aims to preserve text styles while altering the content.

Style Transfer Text Style Transfer +1

Improving Knowledge-aware Dialogue Generation via Knowledge Base Question Answering

1 code implementation16 Dec 2019 Jian Wang, Junhao Liu, Wei Bi, Xiaojiang Liu, Kejing He, Ruifeng Xu, Min Yang

In this paper, we propose a novel knowledge-aware dialogue generation model (called TransDG), which transfers question representation and knowledge matching abilities from knowledge base question answering (KBQA) task to facilitate the utterance understanding and factual knowledge selection for dialogue generation.

Dialogue Generation Knowledge Base Question Answering +1

Relevance-Promoting Language Model for Short-Text Conversation

no code implementations26 Nov 2019 Xin Li, Piji Li, Wei Bi, Xiaojiang Liu, Wai Lam

In this paper, we propose to formulate the STC task as a language modeling problem and tailor-make a training strategy to adapt a language model for response generation.

Language Modelling Response Generation +1

A Discrete CVAE for Response Generation on Short-Text Conversation

no code implementations IJCNLP 2019 Jun Gao, Wei Bi, Xiaojiang Liu, Junhui Li, Guodong Zhou, Shuming Shi

In this paper, we introduce a discrete latent variable with an explicit semantic meaning to improve the CVAE on short-text conversation.

Response Generation Short-Text Conversation +1

Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework

no code implementations IJCNLP 2019 Deng Cai, Yan Wang, Wei Bi, Zhaopeng Tu, Xiaojiang Liu, Shuming Shi

End-to-end sequence generation is a popular technique for developing open domain dialogue systems, though they suffer from the \textit{safe response problem}.

Response Generation

Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks

1 code implementation ACL 2020 Yiping Song, Zequn Liu, Wei Bi, Rui Yan, Ming Zhang

Training the generative models with minimal corpus is one of the critical challenges for building open-domain dialogue systems.

Dialogue Generation Language Modelling +1

Subword ELMo

no code implementations18 Sep 2019 Jiangtong Li, Hai Zhao, Zuchao Li, Wei Bi, Xiaojiang Liu

Embedding from Language Models (ELMo) has shown to be effective for improving many natural language processing (NLP) tasks, and ELMo takes character information to compose word representation to train language models. However, the character is an insufficient and unnatural linguistic unit for word representation. Thus we introduce Embedding from Subword-aware Language Models (ESuLMo) which learns word representation from subwords using unsupervised segmentation over words. We show that ESuLMo can enhance four benchmark NLP tasks more effectively than ELMo, including syntactic dependency parsing, semantic role labeling, implicit discourse relation recognition and textual entailment, which brings a meaningful improvement over ELMo.

Dependency Parsing Natural Language Inference +2

Fine-Grained Sentence Functions for Short-Text Conversation

no code implementations ACL 2019 Wei Bi, Jun Gao, Xiaojiang Liu, Shuming Shi

Classification models are trained on this dataset to (i) recognize the sentence function of new data in a large corpus of short-text conversations; (ii) estimate a proper sentence function of the response given a test query.

Information Retrieval Short-Text Conversation

Learning to Abstract for Memory-augmented Conversational Response Generation

1 code implementation ACL 2019 Zhiliang Tian, Wei Bi, Xiaopeng Li, Nevin L. Zhang

In this work, we propose a memory-augmented generative model, which learns to abstract from the training corpus and saves the useful information to the memory to assist the response generation.

Conversational Response Generation Informativeness +1

Unsupervised Rewriter for Multi-Sentence Compression

no code implementations ACL 2019 Yang Zhao, Xiaoyu Shen, Wei Bi, Akiko Aizawa

First, the word graph approach that simply concatenates fragments from multiple sentences may yield non-fluent or ungrammatical compression.

Sentence Compression

Are Training Samples Correlated? Learning to Generate Dialogue Responses with Multiple References

no code implementations ACL 2019 Lisong Qiu, Juntao Li, Wei Bi, Dongyan Zhao, Rui Yan

Due to its potential applications, open-domain dialogue generation has become popular and achieved remarkable progress in recent years, but sometimes suffers from generic responses.

Dialogue Generation

Generating Multiple Diverse Responses for Short-Text Conversation

no code implementations14 Nov 2018 Jun Gao, Wei Bi, Xiaojiang Liu, Junhui Li, Shuming Shi

In this paper, we propose a novel response generation model, which considers a set of responses jointly and generates multiple diverse responses simultaneously.

Informativeness reinforcement-learning +2

Towards Less Generic Responses in Neural Conversation Models: A Statistical Re-weighting Method

1 code implementation EMNLP 2018 Yahui Liu, Wei Bi, Jun Gao, Xiaojiang Liu, Jian Yao, Shuming Shi

We observe that in the conversation tasks, each query could have multiple responses, which forms a 1-to-n or m-to-n relationship in the view of the total corpus.

Dialogue Generation Machine Translation +1

Language Style Transfer from Sentences with Arbitrary Unknown Styles

no code implementations13 Aug 2018 Yanpeng Zhao, Wei Bi, Deng Cai, Xiaojiang Liu, Kewei Tu, Shuming Shi

Then, by recombining the content with the target style, we decode a sentence aligned in the target domain.

Sentence ReWriting Style Transfer

Mandatory Leaf Node Prediction in Hierarchical Multilabel Classification

no code implementations NeurIPS 2012 Wei Bi, James T. Kwok

However, while there have been a lot of MLNP methods in hierarchical multiclass classification, performing MLNP in hierarchical multilabel classification is much more difficult.

Classification General Classification

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